Please note that authorship is alphabetical. Contributions are listed below - see github for details and who to blame for what :-).
Ben Anderson (b.anderson@soton.ac.uk @dataknut)
If you wish to refer to any of the material from this report please cite as:
Report circulation:
Report purpose:
official New Zealand Air Quality dataThis work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 700386 (SPATIALEC).
This work is (c) 2019 the University of Southampton.
PM 10 data: has more sensors and wider coverage.
Data source: https://data.mfe.govt.nz/data/category/air/
Data file: mfe-pm10-concentrations-200617-CSV/pm10-concentrations-200617.csv
df <- paste0(dPath, pm10File)
pm10dt <- data.table::fread(df)
pm10dt[, `:=`(ba_date, lubridate::as_date(date))]
# the data is daily but there may be gaps?
pm10dt[, `:=`(council.site, paste0(council, ".", site))]
Overall there are:
# looks like daily data with gaps
p <- makeTilePlot(pm10dt, yvar = "pm10", byvar = "council.site")
p + labs(y = "pm10") + guides(fill = guide_legend(title = "pm10"))
Figure 3.1: Test data values by date and site
# looks like daily data with gaps
p <- makeLinePlot(pm10dt, yvar = "pm10", byvar = "council.site")
p <- p + labs(y = "pm10") + guides(colour = guide_legend(title = "pm10"))
plotly::ggplotly(p)